In the digital era, many personal content videos have been transferred to a digital format for storage. There is a strong need to improve picture quality in these videos. Information of temporal relations (motion information) between video frames plays a very important role for such a quality improving process. However, existing algorithms for motion information focus mainly on video compression instead of true motion between pictures. In video compression, the true motion is not a concern, the goal is to find the closest picture data to the original; essentially, which area should be selected to be compared to provide the best compression.
Motion compensated coding is used for moving pictures such as HDTV broadcasting systems and standardized schemes of the Moving Picture Experts Group (MPEG). Motion estimation methods used in motion-compensated coding include a pel-recursive algorithm and a block matching algorithm. Although a pel-recursive algorithm tends to be more precise, block matching is widely used for moving image systems in view of real-time processing and simplified circuit implementation. In using the block matching algorithm, an image is partitioned into blocks having a constant size such as 16 pixels×16 pixels and then a motion vector for each block is obtained using a minimum absolute error.
Another known motion estimation technique uses phase correlation. The aim of the phase correlation process is to measure the movement of objects between two scenes by measuring the correlation of the pixels in the current block against the pixels in the delayed reference block. Where there is simple movement within the block, the correlation will be good, and there will be a peak in the correlation surface.
Currently, the existing motion search algorithms are directly applied for video compression to improve quality which is not very effective. Other times an optical flow approach is utilized which requires too much computational power for practical use. In the optical flow approach, every pixel in the image and the motion of each pixel is used. This approach is extremely complex and requires a huge amount of computation.